{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2B. Data Exploration: Seasonality\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cmx\n",
    "import matplotlib.colors as colors\n",
    "import seaborn as sb\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import csv\n",
    "from datetime import datetime\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall Goal:\n",
    "\n",
    "The goal of the seasonality analysis is to flush out the model that we used for predicting AirBnB pricing. Prices obviously change over time, so this added level of specificity makes the model that much more usable. Obviously, this is a problem that AirBnB has been actively trying to incorporate into their pricing analysis. While we did seem a high level of predictive power for our model, to be able to generalize it to an entire year instead of one snapshot at a specific date is obviously very important. Also, we are hoping with the seasonal nature of the data, utilizing such analysis will help decrease the residuals of our predictors by allowing for more detailed predictions based on more factors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Importing Datafile and converting to numbers\n",
    "data = pd.read_csv('../datasets/calendar_clean.csv')\n",
    "data['new price']=data['new price'].str.lstrip('$').astype(float)\n",
    "data['date']=pd.to_datetime(data['date'])\n",
    "data['weekday'] = pd.Series(data.date).dt.dayofweek"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "This is what the dataset looks like right now. We needed to make changes to turn price into a float, date into a Python Datetime object, and create a column telling us what day of the week each date represents in the \"day of week\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>new price</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3604481.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>t</td>\n",
       "      <td>600.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3604481.0</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>t</td>\n",
       "      <td>600.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3604481.0</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>t</td>\n",
       "      <td>600.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3604481.0</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>t</td>\n",
       "      <td>600.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3604481.0</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>t</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id       date available  new price  weekday\n",
       "0   3604481.0 2015-01-01         t      600.0        3\n",
       "1   3604481.0 2015-01-02         t      600.0        4\n",
       "2   3604481.0 2015-01-03         t      600.0        5\n",
       "3   3604481.0 2015-01-04         t      600.0        6\n",
       "4   3604481.0 2015-01-05         t      600.0        0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xefcf2b0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V+LUQoqQ37btdzr1mnkZvvIhwno7a/qSb+aSf6Na2OtYUk8w4u9n59eop3nbO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\nEL8FXkMZDpdioGGLpTmeRoFpUVBZ3Nu69iqVg1w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LL2IPG0aySpDcbntyPv0Ec8N6Pxlq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\nGG00NBqNRtNj/j8uzVt+po8/awAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd8aada0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Taking average/median values as well as plotting\n",
    "date=[]\n",
    "avg_price=[]\n",
    "median_price=[]\n",
    "for i in data['date'].unique():\n",
    "    date.append(i)\n",
    "    avg_price.append(data[data['date'] == i]['new price'].mean())\n",
    "    median_price.append(data[data['date'] == i]['new price'].median())\n",
    "plt.plot(range(len(avg_price)), avg_price, label= \"Average\")\n",
    "plt.plot(range(len(avg_price)),median_price,color='red', label = \"Median\")\n",
    "plt.ylabel('Price($)')\n",
    "plt.xlabel('Day of 2015')\n",
    "plt.title('Average and Median Price of 2015 AirBnB Listings')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see very interesting trends that occur. Overall, it seems like the trends are very cyclical in nature-- most clearly from a weekly perspective. We then look at a trend of the first 4 weeks of 2015 to get a better idea of whether the cycles are indeed weekly. Secondly, it is worth noting that besides the first day or two where the price is extremely high(perhaps this has to do with New Years), the only only time we see a notable trend in prices, other than the obvious cyclicality, is a depression in the general prices in the first two months or so. Thus in order of priority, we analyze seasonality based on the day of the weeks, important holidays, and then months. For this exploration we will focus on days of the week. We initally look at both average and median to make sure that the trends are similar and the data isn't extremely skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xccc7208>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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orWuAY4HVWmun1roFKAYKgxWAbxdZIYQYT06Xm+17G5iWEMn0pKgB17FYLCzK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc909780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(avg_price[0:70])), avg_price[0:70], label= \"Average\")\n",
    "plt.plot(range(len(avg_price[0:70])),median_price[0:70],color='red', label = \"Median\")\n",
    "plt.ylabel('Price($)')\n",
    "plt.xlabel('Day of 2015')\n",
    "plt.title('Average and Median Price of First 70 Days of AirBnB Listings')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can clearly see the weekly cyclicality trends of the data. Obviously there is a difference between the mean and median prices, but overall the trend is very much similar. Thus, we can move forward with the average AirBnB price because that is what all our baseline/ensemblem models are predicting. Thus, we want to be able to make changes to that predicted average. Clearly there are peaks around once every 7 days: 7, 14, 21, etc. Thus, we will look at days of the week first and see how that impacts the average AirBnB prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xceb3828>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JTwObAQ/38vOYmVkfdJsUJF0O/B/pzv6JDu99ADhM0uci4qCujhER10lap2L55bz/9sDR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\nUXPAwWmVgSsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcd86c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_avg_price=[]\n",
    "daily_median_price=[]\n",
    "b=['Mon','Tue','Wed','Thu','Fri','Sat','Sun']\n",
    "for i in range(7):\n",
    "    daily_avg_price.append(data[data['weekday'] == i]['new price'].mean())\n",
    "    daily_median_price.append(data[data['weekday'] == i]['new price'].median())\n",
    "plt.bar(range(len(daily_avg_price)),daily_avg_price,1/1.5)\n",
    "plt.xticks(range(len(daily_avg_price)),b)\n",
    "plt.ylabel('Average Price($)')\n",
    "plt.xlabel('Day of the Week')\n",
    "plt.title('Average Price per Day of the Week')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xcb0f828>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gZSg6EIfn+VOAY/Mf/qHA1eWEV7iV1MZ7Y56eBIwkHTOvk5LurrmmcAowEXgS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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf26438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(daily_avg_price)),daily_median_price,1/1.5)\n",
    "plt.xticks(range(len(daily_avg_price)),b)\n",
    "plt.ylabel('Median Price($)')\n",
    "plt.xlabel('Day of the Week')\n",
    "plt.title('Median Price per Day of the Week')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "multiplier = []\n",
    "for i in range(7):\n",
    "    multiplier.append(daily_avg_price[i]/daily_avg_price[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.999677044441654,\n",
       " 0.999581906843472,\n",
       " 1.001452750623622,\n",
       " 1.0314801624309469,\n",
       " 1.031393687509484,\n",
       " 1.0023139513392798]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiplier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pricing_data=dict.fromkeys(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in pricing_data:\n",
    "    pricing_data[i]=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Alternate version where \n",
    "listing_id=[]\n",
    "for i in data['listing_id'].unique():\n",
    "    listing_id.append(i)\n",
    "    for index,j in enumerate(b):\n",
    "        pricing_data[j].append(data[(data['weekday'] == index) & (data['listing_id'] == i)]['new price'].mean())\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "results = pd.DataFrame(pricing_data)\n",
    "results['listing_id'] = listing_id\n",
    "results_nona = results.dropna(axis = 0)\n",
    "results_nona = results_nona[['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun', 'listing_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mon</th>\n",
       "      <th>Tue</th>\n",
       "      <th>Wed</th>\n",
       "      <th>Thu</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "      <th>listing_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>3604481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>2949128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.576923</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.566038</td>\n",
       "      <td>80.384615</td>\n",
       "      <td>80.384615</td>\n",
       "      <td>70.576923</td>\n",
       "      <td>4325397.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>275.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>4325398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110.697674</td>\n",
       "      <td>109.756098</td>\n",
       "      <td>110.138889</td>\n",
       "      <td>111.184211</td>\n",
       "      <td>112.361111</td>\n",
       "      <td>111.944444</td>\n",
       "      <td>110.581395</td>\n",
       "      <td>3426149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>65562.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>2730672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>4587554.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>819206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>260.931818</td>\n",
       "      <td>256.521739</td>\n",
       "      <td>258.711111</td>\n",
       "      <td>268.045455</td>\n",
       "      <td>280.333333</td>\n",
       "      <td>283.133333</td>\n",
       "      <td>260.409091</td>\n",
       "      <td>2359340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>4653104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>442.500000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>439.655172</td>\n",
       "      <td>439.655172</td>\n",
       "      <td>442.500000</td>\n",
       "      <td>3276860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>101.351351</td>\n",
       "      <td>101.388889</td>\n",
       "      <td>101.351351</td>\n",
       "      <td>101.351351</td>\n",
       "      <td>3604551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>48.860000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>48.860000</td>\n",
       "      <td>48.860000</td>\n",
       "      <td>48.860000</td>\n",
       "      <td>3997768.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4128844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>185698.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>320.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>320.566038</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>3866715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>2752604.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>262238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>101.372549</td>\n",
       "      <td>101.960784</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>101.400000</td>\n",
       "      <td>101.764706</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>101.938776</td>\n",
       "      <td>4325480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>1048682.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>115.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>1835122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>104.285714</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>104.285714</td>\n",
       "      <td>65660.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>3801214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>3932288.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>4653187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>327816.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>753687.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>85.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>4653196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>1704079.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2783</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1586304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2784</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>4666498.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2785</th>\n",
       "      <td>795.000000</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>1586307.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2786</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>1881556.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2787</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>141.603774</td>\n",
       "      <td>141.634615</td>\n",
       "      <td>141.634615</td>\n",
       "      <td>4469911.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2788</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.943396</td>\n",
       "      <td>200.961538</td>\n",
       "      <td>200.961538</td>\n",
       "      <td>4404384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2789</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>2176166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2790</th>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>4076712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2791</th>\n",
       "      <td>220.588235</td>\n",
       "      <td>219.711538</td>\n",
       "      <td>219.711538</td>\n",
       "      <td>220.192308</td>\n",
       "      <td>220.192308</td>\n",
       "      <td>220.098039</td>\n",
       "      <td>220.588235</td>\n",
       "      <td>13488.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2792</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>4666550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2793</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1990175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2794</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>1127619.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2795</th>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>3290309.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2796</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3224777.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2797</th>\n",
       "      <td>89.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>2897100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2798</th>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>4404433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2799</th>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>3764849.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2800</th>\n",
       "      <td>146.470588</td>\n",
       "      <td>145.882353</td>\n",
       "      <td>146.470588</td>\n",
       "      <td>146.470588</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>146.666667</td>\n",
       "      <td>3028184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2801</th>\n",
       "      <td>175.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>3028186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2802</th>\n",
       "      <td>220.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>208.125000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>3868879.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2803</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>3683551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2804</th>\n",
       "      <td>130.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>3027838.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2805</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1717499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2806</th>\n",
       "      <td>159.666667</td>\n",
       "      <td>159.666667</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>159.666667</td>\n",
       "      <td>159.655172</td>\n",
       "      <td>159.666667</td>\n",
       "      <td>3093762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2807</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1914115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2808</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3486983.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2809</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>2045194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2810</th>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>2296024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2811</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1586454.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2813</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>4207906.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2730 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Mon         Tue         Wed         Thu         Fri         Sat  \\\n",
       "0     600.000000  600.000000  600.000000  600.000000  600.000000  600.000000   \n",
       "1     100.000000  100.000000  100.000000  100.000000  100.000000  100.000000   \n",
       "2      70.576923   70.000000   70.000000   70.566038   80.384615   80.384615   \n",
       "3     275.000000  275.000000  275.000000  275.000000  275.000000  275.000000   \n",
       "4     110.697674  109.756098  110.138889  111.184211  112.361111  111.944444   \n",
       "5      97.000000   97.000000   97.000000   97.000000   97.000000   97.000000   \n",
       "6      58.000000   58.000000   58.000000   58.000000   58.000000   58.000000   \n",
       "7      55.000000   55.000000   55.000000   55.000000   55.000000   55.000000   \n",
       "8      70.000000   70.000000   70.000000   70.000000   70.000000   70.000000   \n",
       "10    260.931818  256.521739  258.711111  268.045455  280.333333  283.133333   \n",
       "11    150.000000  150.000000  150.000000  150.000000  150.000000  150.000000   \n",
       "12    442.500000  440.000000  440.000000  440.000000  439.655172  439.655172   \n",
       "13    100.000000  100.000000  100.000000  101.351351  101.388889  101.351351   \n",
       "14     48.860000   49.000000   49.000000   49.000000   48.860000   48.860000   \n",
       "15    100.000000  100.000000  100.000000  100.000000  130.000000  130.000000   \n",
       "16     50.000000   50.000000   50.000000   50.000000   50.000000   50.000000   \n",
       "17    320.000000  320.000000  320.000000  320.566038  320.000000  320.000000   \n",
       "18    100.000000  100.000000  100.000000  100.000000  100.000000  100.000000   \n",
       "19    168.000000  168.000000  168.000000  168.000000  168.000000  168.000000   \n",
       "21    101.372549  101.960784  102.000000  101.400000  101.764706  102.000000   \n",
       "22     65.000000   65.000000   65.000000   65.000000   65.000000   65.000000   \n",
       "23    115.000000  115.000000  115.000000  115.000000  115.000000  115.000000   \n",
       "24    104.285714  100.000000  100.000000  100.000000  100.000000  105.000000   \n",
       "25    180.000000  180.000000  180.000000  180.000000  180.000000  180.000000   \n",
       "26    165.000000  165.000000  165.000000  165.000000  165.000000  165.000000   \n",
       "27    150.000000  150.000000  150.000000  150.000000  150.000000  150.000000   \n",
       "28    110.000000  110.000000  110.000000  110.000000  125.000000  125.000000   \n",
       "29    125.000000  125.000000  125.000000  125.000000  125.000000  125.000000   \n",
       "30     85.000000   85.000000   85.000000   85.000000  110.000000  110.000000   \n",
       "31    150.000000  150.000000  150.000000  150.000000  150.000000  150.000000   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "2783  200.000000  200.000000  200.000000  200.000000  200.000000  200.000000   \n",
       "2784   75.000000   75.000000   75.000000   75.000000   75.000000   75.000000   \n",
       "2785  795.000000  795.000000  795.000000  795.000000  795.000000  795.000000   \n",
       "2786  110.000000  110.000000  110.000000  110.000000  110.000000  110.000000   \n",
       "2787  140.000000  140.000000  140.000000  140.000000  141.603774  141.634615   \n",
       "2788  200.000000  200.000000  200.000000  200.000000  200.943396  200.961538   \n",
       "2789   60.000000   60.000000   60.000000   60.000000   60.000000   60.000000   \n",
       "2790  550.000000  550.000000  550.000000  550.000000  550.000000  550.000000   \n",
       "2791  220.588235  219.711538  219.711538  220.192308  220.192308  220.098039   \n",
       "2792  183.000000  183.000000  183.000000  183.000000  183.000000  183.000000   \n",
       "2793   99.000000   99.000000   99.000000   99.000000   99.000000   99.000000   \n",
       "2794  150.000000  150.000000  150.000000  150.000000  150.000000  150.000000   \n",
       "2795  250.000000  250.000000  250.000000  250.000000  250.000000  250.000000   \n",
       "2796  200.000000  200.000000  200.000000  200.000000  250.000000  250.000000   \n",
       "2797   89.000000   89.000000   89.000000   89.000000   89.000000   89.000000   \n",
       "2798  225.000000  225.000000  225.000000  225.000000  225.000000  225.000000   \n",
       "2799  180.000000  180.000000  180.000000  180.000000  180.000000  180.000000   \n",
       "2800  146.470588  145.882353  146.470588  146.470588  195.000000  195.000000   \n",
       "2801  175.000000  175.000000  175.000000  175.000000  175.000000  175.000000   \n",
       "2802  220.000000  220.000000  220.000000  220.000000  208.125000  220.000000   \n",
       "2803   65.000000   65.000000   65.000000   65.000000   70.000000   70.000000   \n",
       "2804  130.000000  130.000000  130.000000  130.000000  130.000000  130.000000   \n",
       "2805   60.000000   60.000000   60.000000   60.000000   60.000000   60.000000   \n",
       "2806  159.666667  159.666667  160.000000  160.000000  159.666667  159.655172   \n",
       "2807   80.000000   80.000000   80.000000   80.000000   80.000000   80.000000   \n",
       "2808  100.000000  100.000000  100.000000  100.000000  100.000000  100.000000   \n",
       "2809  150.000000  150.000000  150.000000  150.000000  150.000000  150.000000   \n",
       "2810  240.000000  240.000000  240.000000  240.000000  240.000000  240.000000   \n",
       "2811   48.000000   48.000000   48.000000   48.000000   48.000000   48.000000   \n",
       "2813   90.000000   90.000000   90.000000   90.000000   90.000000   90.000000   \n",
       "\n",
       "             Sun  listing_id  \n",
       "0     600.000000   3604481.0  \n",
       "1     100.000000   2949128.0  \n",
       "2      70.576923   4325397.0  \n",
       "3     275.000000   4325398.0  \n",
       "4     110.581395   3426149.0  \n",
       "5      97.000000     65562.0  \n",
       "6      58.000000   2730672.0  \n",
       "7      55.000000   4587554.0  \n",
       "8      70.000000    819206.0  \n",
       "10    260.409091   2359340.0  \n",
       "11    150.000000   4653104.0  \n",
       "12    442.500000   3276860.0  \n",
       "13    101.351351   3604551.0  \n",
       "14     48.860000   3997768.0  \n",
       "15    100.000000   4128844.0  \n",
       "16     50.000000    185698.0  \n",
       "17    320.000000   3866715.0  \n",
       "18    100.000000   2752604.0  \n",
       "19    168.000000    262238.0  \n",
       "21    101.938776   4325480.0  \n",
       "22     65.000000   1048682.0  \n",
       "23    115.000000   1835122.0  \n",
       "24    104.285714     65660.0  \n",
       "25    180.000000   3801214.0  \n",
       "26    165.000000   3932288.0  \n",
       "27    150.000000   4653187.0  \n",
       "28    110.000000    327816.0  \n",
       "29    125.000000    753687.0  \n",
       "30     85.000000   4653196.0  \n",
       "31    150.000000   1704079.0  \n",
       "...          ...         ...  \n",
       "2783  200.000000   1586304.0  \n",
       "2784   75.000000   4666498.0  \n",
       "2785  795.000000   1586307.0  \n",
       "2786  110.000000   1881556.0  \n",
       "2787  141.634615   4469911.0  \n",
       "2788  200.961538   4404384.0  \n",
       "2789   60.000000   2176166.0  \n",
       "2790  550.000000   4076712.0  \n",
       "2791  220.588235     13488.0  \n",
       "2792  183.000000   4666550.0  \n",
       "2793   99.000000   1990175.0  \n",
       "2794  150.000000   1127619.0  \n",
       "2795  250.000000   3290309.0  \n",
       "2796  200.000000   3224777.0  \n",
       "2797   89.000000   2897100.0  \n",
       "2798  225.000000   4404433.0  \n",
       "2799  180.000000   3764849.0  \n",
       "2800  146.666667   3028184.0  \n",
       "2801  175.000000   3028186.0  \n",
       "2802  220.000000   3868879.0  \n",
       "2803   65.000000   3683551.0  \n",
       "2804  130.000000   3027838.0  \n",
       "2805   60.000000   1717499.0  \n",
       "2806  159.666667   3093762.0  \n",
       "2807   80.000000   1914115.0  \n",
       "2808  100.000000   3486983.0  \n",
       "2809  150.000000   2045194.0  \n",
       "2810  240.000000   2296024.0  \n",
       "2811   48.000000   1586454.0  \n",
       "2813   90.000000   4207906.0  \n",
       "\n",
       "[2730 rows x 8 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_nona"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Something we notice from the Seasonality data is that much of the average prices by listing actually dont fluctuate over time. This explains a lot of why the original changes we from the average prices(across all listings) by day overall is pretty similar-- with a max of around 3% higher on Fridays and Saturdays compared to monday. Looking at this table also allows us to see a lot of business appeal! The fact that so many listings are not fluctuating their prices based on the day of the week shows some pricing inconsistencies with we have established is a necessary phenomena-- dynamic pricing. The fact that so many of these Day of the Week averages for each listing are exactly the same suggests that these listings do not change their prices for any of the reason. Clearly, these listings could use the results of our analysis to incorporate dynamic pricing in their prices over time to help optimize their profits. We export the dataframe for use in our later modeling file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results_nona.to_csv(\"../datasets/seasonality_tomodel.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'price':avg_price})\n",
    "df.to_csv(\"../datasets/daily_price.csv\",index=False)"
   ]
  }
 ],
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